IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v11y2019i12p3334-d240375.html
   My bibliography  Save this article

Analysis of Factors Affecting Real-Time Ridesharing Vehicle Crash Severity

Author

Listed:
  • Bei Zhou

    (School of Highway, Chang’an University, Xi’an 710064, China)

  • Xinfen Zhang

    (School of Highway, Chang’an University, Xi’an 710064, China)

  • Shengrui Zhang

    (School of Highway, Chang’an University, Xi’an 710064, China)

  • Zongzhi Li

    (Department of Civil, Architectural, and Environmental Engineering, Illinois Institute of Technology, Chicago, IL 60616, USA)

  • Xin Liu

    (School of Highway, Chang’an University, Xi’an 710064, China)

Abstract

The popular real-time ridesharing service has promoted social and environmental sustainability in various ways. Meanwhile, it also brings some traffic safety concerns. This paper aims to analyze factors affecting real-time ridesharing vehicle crash severity based on the classification and regression tree (CART) model. The Chicago police-reported crash data from January to December 2018 is collected. Crash severity in the original dataset is highly imbalanced: only 60 out of 2624 crashes are severe injury crashes. To fix the data imbalance problem, a hybrid data preprocessing approach which combines the over- and under-sampling is applied. Model results indicate that, by resampling the crash data, the successfully predicted severe crashes are increased from 0 to 40. Besides, the G-mean is increased from 0% to 73%, and the AUC (area under the receiver operating characteristics curve) is increased from 0.73 to 0.82. The classification tree reveals that following variables are the primary indicators of real-time ridesharing vehicle crash severity: pedestrian/pedalcyclist involvement, number of passengers, weather condition, trafficway type, vehicle manufacture year, traffic control device, driver gender, lighting condition, vehicle type, driver age and crash time. The current study could provide some valuable insights for the sustainable development of real-time ridesharing services and urban transportation.

Suggested Citation

  • Bei Zhou & Xinfen Zhang & Shengrui Zhang & Zongzhi Li & Xin Liu, 2019. "Analysis of Factors Affecting Real-Time Ridesharing Vehicle Crash Severity," Sustainability, MDPI, vol. 11(12), pages 1-15, June.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:12:p:3334-:d:240375
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/11/12/3334/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/11/12/3334/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Yu, Biying & Ma, Ye & Xue, Meimei & Tang, Baojun & Wang, Bin & Yan, Jinyue & Wei, Yi-Ming, 2017. "Environmental benefits from ridesharing: A case of Beijing," Applied Energy, Elsevier, vol. 191(C), pages 141-152.
    2. Ma, Rui & Zhang, H.M., 2017. "The morning commute problem with ridesharing and dynamic parking charges," Transportation Research Part B: Methodological, Elsevier, vol. 106(C), pages 345-374.
    3. Bei Zhou & Zongzhi Li & Shengrui Zhang & Xinfen Zhang & Xin Liu & Qiannan Ma, 2019. "Analysis of Factors Affecting Hit-and-Run and Non-Hit-and-Run in Vehicle-Bicycle Crashes: A Non-Parametric Approach Incorporating Data Imbalance Treatment," Sustainability, MDPI, vol. 11(5), pages 1-14, March.
    4. Furuhata, Masabumi & Dessouky, Maged & Ordóñez, Fernando & Brunet, Marc-Etienne & Wang, Xiaoqing & Koenig, Sven, 2013. "Ridesharing: The state-of-the-art and future directions," Transportation Research Part B: Methodological, Elsevier, vol. 57(C), pages 28-46.
    5. Agatz, Niels & Erera, Alan & Savelsbergh, Martin & Wang, Xing, 2012. "Optimization for dynamic ride-sharing: A review," European Journal of Operational Research, Elsevier, vol. 223(2), pages 295-303.
    6. John M. Barrios & Yael Hochberg & Hanyi Yi, 2020. "The Cost of Convenience: Ridehailing and Traffic Fatalities," NBER Working Papers 26783, National Bureau of Economic Research, Inc.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Sanghoon Lee & Keunho Choi & Donghee Yoo, 2020. "Predicting the Insolvency of SMEs Using Technological Feasibility Assessment Information and Data Mining Techniques," Sustainability, MDPI, vol. 12(23), pages 1-17, November.
    2. Giovanny Pillajo-Quijia & Blanca Arenas-Ramírez & Camino González-Fernández & Francisco Aparicio-Izquierdo, 2020. "Influential Factors on Injury Severity for Drivers of Light Trucks and Vans with Machine Learning Methods," Sustainability, MDPI, vol. 12(4), pages 1-28, February.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Mourad, Abood & Puchinger, Jakob & Chu, Chengbin, 2019. "A survey of models and algorithms for optimizing shared mobility," Transportation Research Part B: Methodological, Elsevier, vol. 123(C), pages 323-346.
    2. Meng Li & Guowei Hua & Haijun Huang, 2018. "A Multi-Modal Route Choice Model with Ridesharing and Public Transit," Sustainability, MDPI, vol. 10(11), pages 1-14, November.
    3. Zhong, Lin & Zhang, Kenan & (Marco) Nie, Yu & Xu, Jiuping, 2020. "Dynamic carpool in morning commute: Role of high-occupancy-vehicle (HOV) and high-occupancy-toll (HOT) lanes," Transportation Research Part B: Methodological, Elsevier, vol. 135(C), pages 98-119.
    4. Wang, Jing-Peng & Ban, Xuegang (Jeff) & Huang, Hai-Jun, 2019. "Dynamic ridesharing with variable-ratio charging-compensation scheme for morning commute," Transportation Research Part B: Methodological, Elsevier, vol. 122(C), pages 390-415.
    5. Li, Yuanyuan & Liu, Yang, 2021. "Optimizing flexible one-to-two matching in ride-hailing systems with boundedly rational users," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 150(C).
    6. Lei, Chao & Jiang, Zhoutong & Ouyang, Yanfeng, 2020. "Path-based dynamic pricing for vehicle allocation in ridesharing systems with fully compliant drivers," Transportation Research Part B: Methodological, Elsevier, vol. 132(C), pages 60-75.
    7. Sun, S. & Szeto, W.Y., 2021. "Multi-class stochastic user equilibrium assignment model with ridesharing: Formulation and policy implications," Transportation Research Part A: Policy and Practice, Elsevier, vol. 145(C), pages 203-227.
    8. Tang, Zhe-Yi & Tian, Li-Jun & Wang, David Z.W., 2021. "Multi-modal morning commute with endogenous shared autonomous vehicle penetration considering parking space constraint," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 151(C).
    9. Huang, Zhihui & Long, Jiancheng & Szeto, W.Y. & Liu, Haoxiang, 2021. "Modeling and managing the morning commute problem with park-and-ride-sharing," Transportation Research Part B: Methodological, Elsevier, vol. 150(C), pages 190-226.
    10. Cai, Hua & Wang, Xi & Adriaens, Peter & Xu, Ming, 2019. "Environmental benefits of taxi ride sharing in Beijing," Energy, Elsevier, vol. 174(C), pages 503-508.
    11. Qin Yang & Jinfeng Liu & Xing Liu & Cejun Cao & Wei Zhang, 2019. "A Two-Sided Matching Model for Task Distribution in Ridesharing: A Sustainable Operations Perspective," Sustainability, MDPI, vol. 11(7), pages 1-16, April.
    12. Long, Jiancheng & Tan, Weimin & Szeto, W.Y. & Li, Yao, 2018. "Ride-sharing with travel time uncertainty," Transportation Research Part B: Methodological, Elsevier, vol. 118(C), pages 143-171.
    13. Tian, Li-Jun & Sheu, Jiuh-Biing & Huang, Hai-Jun, 2019. "The morning commute problem with endogenous shared autonomous vehicle penetration and parking space constraint," Transportation Research Part B: Methodological, Elsevier, vol. 123(C), pages 258-278.
    14. Daganzo, Carlos F. & Ouyang, Yanfeng, 2019. "A general model of demand-responsive transportation services: From taxi to ridesharing to dial-a-ride," Transportation Research Part B: Methodological, Elsevier, vol. 126(C), pages 213-224.
    15. Yu, Guodong & Liu, Aijun & Zhang, Jianghua & Sun, Huiping, 2021. "Optimal operations planning of electric autonomous vehicles via asynchronous learning in ride-hailing systems," Omega, Elsevier, vol. 103(C).
    16. Yu Wang & Shanyong Wang & Jing Wang & Jiuchang Wei & Chenglin Wang, 2020. "An empirical study of consumers’ intention to use ride-sharing services: using an extended technology acceptance model," Transportation, Springer, vol. 47(1), pages 397-415, February.
    17. Dessouky, Maged M & Hu, Shichun, 2021. "Dynamic Routing for Ride-Sharing," Institute of Transportation Studies, Working Paper Series qt6qq8r7hz, Institute of Transportation Studies, UC Davis.
    18. Meng, Zhiyi & Li, Eldon Y. & Qiu, Rui, 2020. "Environmental sustainability with free-floating carsharing services: An on-demand refueling recommendation system for Car2go in Seattle," Technological Forecasting and Social Change, Elsevier, vol. 152(C).
    19. Zhang, Haoran & Chen, Jinyu & Li, Wenjing & Song, Xuan & Shibasaki, Ryosuke, 2020. "Mobile phone GPS data in urban ride-sharing: An assessment method for emission reduction potential," Applied Energy, Elsevier, vol. 269(C).
    20. Horner, Hannah & Pazour, Jennifer & Mitchell, John E., 2021. "Optimizing driver menus under stochastic selection behavior for ridesharing and crowdsourced delivery," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 153(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:11:y:2019:i:12:p:3334-:d:240375. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.